{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![MLU Logo](../data/MLU_Logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"0\">Machine Learning Accelerator - Tabular Data - Lecture 1</a>\n",
    "\n",
    "\n",
    "## Exploratory data analysis\n",
    "\n",
    "In this notebook, we go through basic steps of exploratory data analysis (EDA), performing initial data investigations to discover patterns, spot anomalies, and look for insights to inform later ML modeling choices.\n",
    "\n",
    "1. <a href=\"#1\">Read the dataset</a>\n",
    "2. <a href=\"#2\">Overall Statistics</a>\n",
    "3. <a href=\"#3\">Univariate Statistics: Basic Plots</a>\n",
    "4. <a href=\"#4\">Multivariate Statistics: Scatter Plots and Correlations</a>\n",
    "5. <a href=\"#5\">Handling Missing Values</a>\n",
    "    * <a href=\"#51\">Drop columns with missing values</a>\n",
    "    * <a href=\"#52\">Drop rows with missing values</a>\n",
    "    * <a href=\"#53\">Impute (fill-in) missing values with .fillna()</a>\n",
    "    * <a href=\"#54\">Impute (fill-in) missing values with sklearn's SimpleImputer</a>\n",
    "    \n",
    "__Austin Animal Center Dataset__:\n",
    "\n",
    "In this exercise, we are working with pet adoption data from __Austin Animal Center__. We have two datasets that cover intake and outcome of animals. Intake data is available from [here](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Intakes/wter-evkm) and outcome is from [here](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Outcomes/9t4d-g238). \n",
    "\n",
    "In order to work with a single table, we joined the intake and outcome tables using the \"Animal ID\" column and created a single __review.csv__ file. We also didn't consider animals with multiple entries to the facility to keep our dataset simple. If you want to see the original datasets and the merged data with multiple entries, they are available under data/review folder: Austin_Animal_Center_Intakes.csv, Austin_Animal_Center_Outcomes.csv and Austin_Animal_Center_Intakes_Outcomes.csv.\n",
    "\n",
    "__Dataset schema:__ \n",
    "- __Pet ID__ - Unique ID of pet\n",
    "- __Outcome Type__ - State of pet at the time of recording the outcome (0 = not placed, 1 = placed). This is the field to predict.\n",
    "- __Sex upon Outcome__ - Sex of pet at outcome\n",
    "- __Name__ - Name of pet \n",
    "- __Found Location__ - Found location of pet before entered the center\n",
    "- __Intake Type__ - Circumstances bringing the pet to the center\n",
    "- __Intake Condition__ - Health condition of pet when entered the center\n",
    "- __Pet Type__ - Type of pet\n",
    "- __Sex upon Intake__ - Sex of pet when entered the center\n",
    "- __Breed__ - Breed of pet \n",
    "- __Color__ - Color of pet \n",
    "- __Age upon Intake Days__ - Age of pet when entered the center (days)\n",
    "- __Age upon Outcome Days__ - Age of pet at outcome (days)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "%pip install -q -r ../requirements.txt --no-deps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. <a name=\"1\">Read the dataset</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n",
    "\n",
    "Let's read the dataset into a dataframe, using Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the dataset is: (95485, 13)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "  \n",
    "df = pd.read_csv('../data/review/review_dataset.csv')\n",
    "\n",
    "print('The shape of the dataset is:', df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. <a name=\"2\">Overall Statistics</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n",
    "\n",
    "We will look at number of rows, columns and some simple statistics of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pet ID</th>\n",
       "      <th>Outcome Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Name</th>\n",
       "      <th>Found Location</th>\n",
       "      <th>Intake Type</th>\n",
       "      <th>Intake Condition</th>\n",
       "      <th>Pet Type</th>\n",
       "      <th>Sex upon Intake</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Age upon Intake Days</th>\n",
       "      <th>Age upon Outcome Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A794011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>Chunk</td>\n",
       "      <td>Austin (TX)</td>\n",
       "      <td>Owner Surrender</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Brown Tabby/White</td>\n",
       "      <td>730</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A776359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>Gizmo</td>\n",
       "      <td>7201 Levander Loop in Austin (TX)</td>\n",
       "      <td>Stray</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>Chihuahua Shorthair Mix</td>\n",
       "      <td>White/Brown</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A674754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12034 Research in Austin (TX)</td>\n",
       "      <td>Stray</td>\n",
       "      <td>Nursing</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Orange Tabby</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A689724</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>*Donatello</td>\n",
       "      <td>2300 Waterway Bnd in Austin (TX)</td>\n",
       "      <td>Stray</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Black</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A680969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>*Zeus</td>\n",
       "      <td>4701 Staggerbrush Rd in Austin (TX)</td>\n",
       "      <td>Stray</td>\n",
       "      <td>Nursing</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>White/Orange Tabby</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pet ID  Outcome Type Sex upon Outcome        Name  \\\n",
       "0  A794011           1.0    Neutered Male       Chunk   \n",
       "1  A776359           1.0    Neutered Male       Gizmo   \n",
       "2  A674754           0.0      Intact Male         NaN   \n",
       "3  A689724           1.0    Neutered Male  *Donatello   \n",
       "4  A680969           1.0    Neutered Male       *Zeus   \n",
       "\n",
       "                        Found Location      Intake Type Intake Condition  \\\n",
       "0                          Austin (TX)  Owner Surrender           Normal   \n",
       "1    7201 Levander Loop in Austin (TX)            Stray           Normal   \n",
       "2        12034 Research in Austin (TX)            Stray          Nursing   \n",
       "3     2300 Waterway Bnd in Austin (TX)            Stray           Normal   \n",
       "4  4701 Staggerbrush Rd in Austin (TX)            Stray          Nursing   \n",
       "\n",
       "  Pet Type Sex upon Intake                    Breed               Color  \\\n",
       "0      Cat   Neutered Male   Domestic Shorthair Mix   Brown Tabby/White   \n",
       "1      Dog     Intact Male  Chihuahua Shorthair Mix         White/Brown   \n",
       "2      Cat     Intact Male   Domestic Shorthair Mix        Orange Tabby   \n",
       "3      Cat     Intact Male   Domestic Shorthair Mix               Black   \n",
       "4      Cat     Intact Male   Domestic Shorthair Mix  White/Orange Tabby   \n",
       "\n",
       "   Age upon Intake Days  Age upon Outcome Days  \n",
       "0                   730                    730  \n",
       "1                   365                    365  \n",
       "2                     6                      6  \n",
       "3                    60                     60  \n",
       "4                     7                     60  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first five rows\n",
    "# NaN means missing data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 95485 entries, 0 to 95484\n",
      "Data columns (total 13 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Pet ID                 95485 non-null  object \n",
      " 1   Outcome Type           95485 non-null  float64\n",
      " 2   Sex upon Outcome       95484 non-null  object \n",
      " 3   Name                   59138 non-null  object \n",
      " 4   Found Location         95485 non-null  object \n",
      " 5   Intake Type            95485 non-null  object \n",
      " 6   Intake Condition       95485 non-null  object \n",
      " 7   Pet Type               95485 non-null  object \n",
      " 8   Sex upon Intake        95484 non-null  object \n",
      " 9   Breed                  95485 non-null  object \n",
      " 10  Color                  95485 non-null  object \n",
      " 11  Age upon Intake Days   95485 non-null  int64  \n",
      " 12  Age upon Outcome Days  95485 non-null  int64  \n",
      "dtypes: float64(1), int64(2), object(10)\n",
      "memory usage: 9.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Let's see the data types and non-null values for each column\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outcome Type</th>\n",
       "      <th>Age upon Intake Days</th>\n",
       "      <th>Age upon Outcome Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>95485.000000</td>\n",
       "      <td>95485.000000</td>\n",
       "      <td>95485.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.564005</td>\n",
       "      <td>703.436959</td>\n",
       "      <td>717.757313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.495889</td>\n",
       "      <td>1052.252197</td>\n",
       "      <td>1055.023160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9125.000000</td>\n",
       "      <td>9125.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Outcome Type  Age upon Intake Days  Age upon Outcome Days\n",
       "count  95485.000000          95485.000000           95485.000000\n",
       "mean       0.564005            703.436959             717.757313\n",
       "std        0.495889           1052.252197            1055.023160\n",
       "min        0.000000              0.000000               0.000000\n",
       "25%        0.000000             30.000000              60.000000\n",
       "50%        1.000000            365.000000             365.000000\n",
       "75%        1.000000            730.000000             730.000000\n",
       "max        1.000000           9125.000000            9125.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This prints basic statistics for numerical columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's separate model features and model target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pet ID', 'Outcome Type', 'Sex upon Outcome', 'Name', 'Found Location',\n",
      "       'Intake Type', 'Intake Condition', 'Pet Type', 'Sex upon Intake',\n",
      "       'Breed', 'Color', 'Age upon Intake Days', 'Age upon Outcome Days'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model features:  Index(['Pet ID', 'Sex upon Outcome', 'Name', 'Found Location', 'Intake Type',\n",
      "       'Intake Condition', 'Pet Type', 'Sex upon Intake', 'Breed', 'Color',\n",
      "       'Age upon Intake Days', 'Age upon Outcome Days'],\n",
      "      dtype='object')\n",
      "Model target:  Outcome Type\n"
     ]
    }
   ],
   "source": [
    "model_features = df.columns.drop('Outcome Type')\n",
    "model_target = 'Outcome Type'\n",
    "\n",
    "print('Model features: ', model_features)\n",
    "print('Model target: ', model_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can explore the features set further, figuring out first what features are numerical or categorical. Beware that some integer-valued features could actually be categorical features, and some categorical features could be text features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical columns: Index(['Age upon Intake Days', 'Age upon Outcome Days'], dtype='object')\n",
      "\n",
      "Categorical columns: Index(['Pet ID', 'Sex upon Outcome', 'Name', 'Found Location', 'Intake Type',\n",
      "       'Intake Condition', 'Pet Type', 'Sex upon Intake', 'Breed', 'Color'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "numerical_features_all = df[model_features].select_dtypes(include=np.number).columns\n",
    "print('Numerical columns:',numerical_features_all)\n",
    "\n",
    "print('')\n",
    "\n",
    "categorical_features_all = df[model_features].select_dtypes(include='object').columns\n",
    "print('Categorical columns:',categorical_features_all)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. <a name=\"3\">Basic Plots</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n",
    "\n",
    "In this section, we examine our data with plots. Important note: These plots ignore null (missing) values. We will learn how to deal with missing values in the next section.\n",
    "\n",
    "\n",
    "__Bar plots__: These plots show counts of categorical data fields. __value_counts()__ function yields the counts of each unique value. It is useful for categorical variables.\n",
    "\n",
    "First, let's look at the distribution of the model target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome Type\n",
       "1.0    53854\n",
       "0.0    41631\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[model_target].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__plot.bar()__ addition to the __value_counts()__ function makes a bar plot of the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df[model_target].value_counts().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now onto the categorical features, exploring number of unique values per feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pet ID\n",
      "A815419    1\n",
      "A794011    1\n",
      "A776359    1\n",
      "A674754    1\n",
      "A689724    1\n",
      "          ..\n",
      "A757815    1\n",
      "A792258    1\n",
      "A802723    1\n",
      "A705205    1\n",
      "A766886    1\n",
      "Name: count, Length: 95485, dtype: int64\n",
      "Sex upon Outcome\n",
      "Neutered Male    30244\n",
      "Spayed Female    28145\n",
      "Intact Female    13724\n",
      "Intact Male      13646\n",
      "Unknown           9725\n",
      "Name: count, dtype: int64\n",
      "Name\n",
      "Bella        338\n",
      "Luna         313\n",
      "Max          311\n",
      "Daisy        239\n",
      "Lucy         223\n",
      "            ... \n",
      "Moebius        1\n",
      "*Alastair      1\n",
      "Cochise        1\n",
      "Blou           1\n",
      "*Shinobi       1\n",
      "Name: count, Length: 17468, dtype: int64\n",
      "Found Location\n",
      "Austin (TX)                                            14833\n",
      "Travis (TX)                                             1402\n",
      "7201 Levander Loop in Austin (TX)                        644\n",
      "Outside Jurisdiction                                     607\n",
      "Del Valle (TX)                                           426\n",
      "                                                       ...  \n",
      "1704 Sanchez St in Austin (TX)                             1\n",
      "Highway 71 And Fm 620 in Travis (TX)                       1\n",
      "2604 Salado Street in Austin (TX)                          1\n",
      "13513 Campasina Dr in Austin (TX)                          1\n",
      "Decker Lake Road And Blue Bluff Road in Austin (TX)        1\n",
      "Name: count, Length: 43951, dtype: int64\n",
      "Intake Type\n",
      "Stray                 70203\n",
      "Owner Surrender       15146\n",
      "Public Assist          5236\n",
      "Wildlife               4554\n",
      "Euthanasia Request      235\n",
      "Abandoned               111\n",
      "Name: count, dtype: int64\n",
      "Intake Condition\n",
      "Normal      81912\n",
      "Injured      5386\n",
      "Sick         4291\n",
      "Nursing      3172\n",
      "Aged          352\n",
      "Other         189\n",
      "Feral          97\n",
      "Pregnant       63\n",
      "Medical        21\n",
      "Behavior        2\n",
      "Name: count, dtype: int64\n",
      "Pet Type\n",
      "Dog          48719\n",
      "Cat          40082\n",
      "Other         6115\n",
      "Bird           553\n",
      "Livestock       16\n",
      "Name: count, dtype: int64\n",
      "Sex upon Intake\n",
      "Intact Male      33369\n",
      "Intact Female    32515\n",
      "Neutered Male    10521\n",
      "Unknown           9725\n",
      "Spayed Female     9354\n",
      "Name: count, dtype: int64\n",
      "Breed\n",
      "Domestic Shorthair Mix                27689\n",
      "Domestic Shorthair                     5076\n",
      "Pit Bull Mix                           5017\n",
      "Chihuahua Shorthair Mix                4963\n",
      "Labrador Retriever Mix                 4789\n",
      "                                      ...  \n",
      "Miniature Schnauzer/Maltese               1\n",
      "Cardigan Welsh Corgi/Cairn Terrier        1\n",
      "Chinchilla-Amer                           1\n",
      "Brittany/Chihuahua Shorthair              1\n",
      "Beagle/Treeing Walker Coonhound           1\n",
      "Name: count, Length: 2395, dtype: int64\n",
      "Color\n",
      "Black/White                  9688\n",
      "Black                        8528\n",
      "Brown Tabby                  6077\n",
      "Brown                        4440\n",
      "White                        3312\n",
      "                             ... \n",
      "Cream/Cream                     1\n",
      "Brown Brindle/Brown Merle       1\n",
      "Tan/Brown Merle                 1\n",
      "Blue/Orange                     1\n",
      "Liver/Buff                      1\n",
      "Name: count, Length: 567, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for c in categorical_features_all: \n",
    "    print(df[c].value_counts())\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the number of unique values (unique IDs for example won't be very useful to visualize, for example), for some categorical features, let's see some bar plot visualizations. For simplicity and speed, here we only show box plots for those features with less than 50 unique values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex upon Outcome\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intake Type\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intake Condition\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pet Type\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex upon Intake\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "for c in categorical_features_all:\n",
    "    if len(df[c].value_counts()) < 50:\n",
    "        print(c)\n",
    "        df[c].value_counts().plot.bar()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Histograms:__ Histograms show distribution of numeric data. Data is divided into \"buckets\" or \"bins\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Intake Days\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Outcome Days\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "for c in numerical_features_all:\n",
    "    print(c)\n",
    "    df[c].plot.hist(bins=5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If for some histograms the values are heavily placed in the first bin, it is good to check for outliers, either checking the min-max values of those particular features and/or explore value ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Intake Days\n",
      "min: 0 max: 9125\n",
      "Age upon Outcome Days\n",
      "min: 0 max: 9125\n"
     ]
    }
   ],
   "source": [
    "for c in numerical_features_all:\n",
    "    print(c)\n",
    "    print('min:', df[c].min(), 'max:', df[c].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With __value_counts()__ function, we can increase the number of histogram bins to 10 for more bins for a more refined view of the numerical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Intake Days\n",
      "(-9.126, 912.5]     74835\n",
      "(912.5, 1825.0]     10647\n",
      "(1825.0, 2737.5]     3471\n",
      "(2737.5, 3650.0]     3998\n",
      "(3650.0, 4562.5]     1234\n",
      "(4562.5, 5475.0]     1031\n",
      "(5475.0, 6387.5]      183\n",
      "(6387.5, 7300.0]       79\n",
      "(7300.0, 8212.5]        5\n",
      "(8212.5, 9125.0]        2\n",
      "Name: count, dtype: int64\n",
      "Age upon Outcome Days\n",
      "(-9.126, 912.5]     74642\n",
      "(912.5, 1825.0]     10699\n",
      "(1825.0, 2737.5]     3465\n",
      "(2737.5, 3650.0]     4080\n",
      "(3650.0, 4562.5]     1263\n",
      "(4562.5, 5475.0]     1061\n",
      "(5475.0, 6387.5]      187\n",
      "(6387.5, 7300.0]       81\n",
      "(7300.0, 8212.5]        5\n",
      "(8212.5, 9125.0]        2\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for c in numerical_features_all: \n",
    "    print(c)\n",
    "    print(df[c].value_counts(bins=10, sort=False))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If any outliers are identified as very likely wrong values, dropping them could improve the numerical values histograms, and later overall model performance. While a good rule of thumb is that anything not in the range of (Q1 - 1.5 IQR) and (Q3 + 1.5 IQR) is an outlier, other rules for removing 'outliers' should be considered as well. For example, removing any values in the upper 1%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Intake Days\n",
      "Age upon Outcome Days\n"
     ]
    }
   ],
   "source": [
    "for c in numerical_features_all:\n",
    "    print(c)\n",
    "    \n",
    "    # Drop values below Q1 - 1.5 IQR and beyond Q3 + 1.5 IQR\n",
    "    #Q1 = df[c].quantile(0.25)\n",
    "    #Q3 = df[c].quantile(0.75)\n",
    "    #IQR = Q3 - Q1\n",
    "    #print (Q1 - 1.5*IQR, Q3 + 1.5*IQR)\n",
    "    \n",
    "    #dropIndexes = df[df[c] > Q3 + 1.5*IQR].index\n",
    "    #df.drop(dropIndexes , inplace=True)\n",
    "    #dropIndexes = df[df[c] < Q1 - 1.5*IQR].index\n",
    "    #df.drop(dropIndexes , inplace=True)\n",
    "    \n",
    "    # Drop values beyond 90% of max()\n",
    "    dropIndexes = df[df[c] > df[c].max()*9/10].index\n",
    "    df.drop(dropIndexes , inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Intake Days\n",
      "(-6.936, 693.5]     61425\n",
      "(693.5, 1387.0]     18400\n",
      "(1387.0, 2080.5]     5657\n",
      "(2080.5, 2774.0]     3471\n",
      "(2774.0, 3467.5]     2557\n",
      "(3467.5, 4161.0]     1962\n",
      "(4161.0, 4854.5]     1148\n",
      "(4854.5, 5548.0]      596\n",
      "(5548.0, 6241.5]      183\n",
      "(6241.5, 6935.0]       63\n",
      "Name: count, dtype: int64\n",
      "Age upon Outcome Days\n",
      "(-6.936, 693.5]     61208\n",
      "(693.5, 1387.0]     18490\n",
      "(1387.0, 2080.5]     5643\n",
      "(2080.5, 2774.0]     3465\n",
      "(2774.0, 3467.5]     2600\n",
      "(3467.5, 4161.0]     2004\n",
      "(4161.0, 4854.5]     1196\n",
      "(4854.5, 5548.0]      604\n",
      "(5548.0, 6241.5]      187\n",
      "(6241.5, 6935.0]       65\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for c in numerical_features_all:\n",
    "    print(c)\n",
    "    print(df[c].value_counts(bins=10, sort=False))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the histograms again, with more bins for vizibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Intake Days\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Outcome Days\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in numerical_features_all:\n",
    "    print(c)\n",
    "    df[c].plot.hist(bins=100)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. <a name=\"4\">Scatter Plots and Correlation</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n",
    "\n",
    "### Scatter plot\n",
    "Scatter plots are simple 2D plots of two numerical variables that can be used to examine the relationship between two variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(len(numerical_features_all), len(numerical_features_all), figsize=(16, 16), sharex=False, sharey=False)\n",
    "for i in range(0,len(numerical_features_all)):\n",
    "    for j in range(0,len(numerical_features_all)):\n",
    "        axes[i,j].scatter(x = df[numerical_features_all[i]], y = df[numerical_features_all[j]])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatterplot with Identification\n",
    "\n",
    "We can also add the target values, 0 or 1, to our scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "X1 = df[[numerical_features_all[0], numerical_features_all[1]]][df[model_target] == 0]\n",
    "X2 = df[[numerical_features_all[0], numerical_features_all[1]]][df[model_target] == 1]\n",
    "\n",
    "plt.scatter(X1.iloc[:,0], \n",
    "            X1.iloc[:,1], \n",
    "            s=50, \n",
    "            c='blue', \n",
    "            marker='o', \n",
    "            label='0')\n",
    "\n",
    "plt.scatter(X2.iloc[:,0], \n",
    "            X2.iloc[:,1], \n",
    "            s=50, \n",
    "            c='red', \n",
    "            marker='v', \n",
    "            label='1')\n",
    "\n",
    "plt.xlabel(numerical_features_all[0])\n",
    "plt.ylabel(numerical_features_all[1])\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatterplots with identification, can sometimes help identify whether or not we can get good separation between the data points, based on these two numerical features alone. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix Heatmat\n",
    "We plot the correlation matrix. Correlation scores are calculated for numerical fields. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_cfd2e_row0_col0, #T_cfd2e_row1_col1 {\n",
       "  background-color: #d9d9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cfd2e_row0_col1, #T_cfd2e_row1_col0 {\n",
       "  background-color: #3182bd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cfd2e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cfd2e_level0_col0\" class=\"col_heading level0 col0\" >Age upon Intake Days</th>\n",
       "      <th id=\"T_cfd2e_level0_col1\" class=\"col_heading level0 col1\" >Age upon Outcome Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cfd2e_level0_row0\" class=\"row_heading level0 row0\" >Age upon Intake Days</th>\n",
       "      <td id=\"T_cfd2e_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_cfd2e_row0_col1\" class=\"data row0 col1\" >0.998839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cfd2e_level0_row1\" class=\"row_heading level0 row1\" >Age upon Outcome Days</th>\n",
       "      <td id=\"T_cfd2e_row1_col0\" class=\"data row1 col0\" >0.998839</td>\n",
       "      <td id=\"T_cfd2e_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f80ec27a440>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=[numerical_features_all[0], numerical_features_all[1]]\n",
    "#print(df[cols].corr())\n",
    "df[cols].corr().style.background_gradient(cmap='tab20c')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to scatterplots, but now the correlation matrix values can more clearly pinpoint relationships between the numerical features. Correlation values of -1 means perfect negative correlation, 1 means perfect positive correlation, and 0 means there is no relationship between the two numerical features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A fancy example using Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from string import ascii_letters\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Generate a large random dataset\n",
    "rs = np.random.RandomState(33)\n",
    "d = pd.DataFrame(data=rs.normal(size=(100, 26)),\n",
    "                 columns=list(ascii_letters[26:]))\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = d.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, more exploratory data analysis might reveal other important hidden atributes and/or relationships of the model features considered. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. <a name=\"5\">Handling Missing Values</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n",
    "\n",
    "  * <a href=\"#51\">Drop columns with missing values</a>\n",
    "  * <a href=\"#52\">Drop rows with missing values</a>\n",
    "  * <a href=\"#53\"> Impute (fill-in) missing values with .fillna()</a>\n",
    "  * <a href=\"#54\"> Impute (fill-in) missing values with sklearn's SimpleImputer</a>\n",
    "\n",
    "Let's first check the number of missing (nan) values for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pet ID                       0\n",
       "Outcome Type                 0\n",
       "Sex upon Outcome             1\n",
       "Name                     36343\n",
       "Found Location               0\n",
       "Intake Type                  0\n",
       "Intake Condition             0\n",
       "Pet Type                     0\n",
       "Sex upon Intake              1\n",
       "Breed                        0\n",
       "Color                        0\n",
       "Age upon Intake Days         0\n",
       "Age upon Outcome Days        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore a few options dealing with missing values, when there are values missing on many features, both numerical and categorical types. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"51\">Drop columns with missing values</a>\n",
    "(<a href=\"#5\">Go to Handling Missing Values</a>)\n",
    "\n",
    "We can drop some feautures/columns if we think there is significant amount of missing data in those features. Here we \n",
    "are dropping features having more than 20% missing values.\n",
    "\n",
    "__Hint:__ You can also use __inplace=True__ parameter to drop features inplace without assignment.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pet ID                   0.000000\n",
      "Outcome Type             0.000000\n",
      "Sex upon Outcome         0.000010\n",
      "Name                     0.380706\n",
      "Found Location           0.000000\n",
      "Intake Type              0.000000\n",
      "Intake Condition         0.000000\n",
      "Pet Type                 0.000000\n",
      "Sex upon Intake          0.000010\n",
      "Breed                    0.000000\n",
      "Color                    0.000000\n",
      "Age upon Intake Days     0.000000\n",
      "Age upon Outcome Days    0.000000\n",
      "dtype: float64\n",
      "Index(['Name'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pet ID</th>\n",
       "      <th>Outcome Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Found Location</th>\n",
       "      <th>Intake Type</th>\n",
       "      <th>Intake Condition</th>\n",
       "      <th>Pet Type</th>\n",
       "      <th>Sex upon Intake</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Age upon Intake Days</th>\n",
       "      <th>Age upon Outcome Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A794011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>Austin (TX)</td>\n",
       "      <td>Owner Surrender</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Brown Tabby/White</td>\n",
       "      <td>730</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A776359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>7201 Levander Loop in Austin (TX)</td>\n",
       "      <td>Stray</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>Chihuahua Shorthair Mix</td>\n",
       "      <td>White/Brown</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A674754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>12034 Research in Austin (TX)</td>\n",
       "      <td>Stray</td>\n",
       "      <td>Nursing</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Orange Tabby</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A689724</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2300 Waterway Bnd in Austin (TX)</td>\n",
       "      <td>Stray</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Black</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A680969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>4701 Staggerbrush Rd in Austin (TX)</td>\n",
       "      <td>Stray</td>\n",
       "      <td>Nursing</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>White/Orange Tabby</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pet ID  Outcome Type Sex upon Outcome  \\\n",
       "0  A794011           1.0    Neutered Male   \n",
       "1  A776359           1.0    Neutered Male   \n",
       "2  A674754           0.0      Intact Male   \n",
       "3  A689724           1.0    Neutered Male   \n",
       "4  A680969           1.0    Neutered Male   \n",
       "\n",
       "                        Found Location      Intake Type Intake Condition  \\\n",
       "0                          Austin (TX)  Owner Surrender           Normal   \n",
       "1    7201 Levander Loop in Austin (TX)            Stray           Normal   \n",
       "2        12034 Research in Austin (TX)            Stray          Nursing   \n",
       "3     2300 Waterway Bnd in Austin (TX)            Stray           Normal   \n",
       "4  4701 Staggerbrush Rd in Austin (TX)            Stray          Nursing   \n",
       "\n",
       "  Pet Type Sex upon Intake                    Breed               Color  \\\n",
       "0      Cat   Neutered Male   Domestic Shorthair Mix   Brown Tabby/White   \n",
       "1      Dog     Intact Male  Chihuahua Shorthair Mix         White/Brown   \n",
       "2      Cat     Intact Male   Domestic Shorthair Mix        Orange Tabby   \n",
       "3      Cat     Intact Male   Domestic Shorthair Mix               Black   \n",
       "4      Cat     Intact Male   Domestic Shorthair Mix  White/Orange Tabby   \n",
       "\n",
       "   Age upon Intake Days  Age upon Outcome Days  \n",
       "0                   730                    730  \n",
       "1                   365                    365  \n",
       "2                     6                      6  \n",
       "3                    60                     60  \n",
       "4                     7                     60  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 2/10\n",
    "print((df.isna().sum()/len(df.index)))\n",
    "columns_to_drop = df.loc[:,list(((df.isna().sum()/len(df.index))>=threshold))].columns    \n",
    "print(columns_to_drop)\n",
    "\n",
    "df_columns_dropped = df.drop(columns_to_drop, axis = 1)  \n",
    "df_columns_dropped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pet ID                   0\n",
       "Outcome Type             0\n",
       "Sex upon Outcome         1\n",
       "Found Location           0\n",
       "Intake Type              0\n",
       "Intake Condition         0\n",
       "Pet Type                 0\n",
       "Sex upon Intake          1\n",
       "Breed                    0\n",
       "Color                    0\n",
       "Age upon Intake Days     0\n",
       "Age upon Outcome Days    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_columns_dropped.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95462, 12)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_columns_dropped.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the reduced size of the dataset features. This can sometimes lead to underfitting models -- not having enough features to build a good model able to capture the pattern in the dataset, especially when dropping features that are essential to the task at hand. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"52\">Drop rows with missing values</a>\n",
    "(<a href=\"#5\">Go to Handling Missing Values</a>)\n",
    "\n",
    "Here, we simply drop rows that have at least one missing value. There are other drop options to explore, depending on specific problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_missing_dropped = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the missing values below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pet ID                   0\n",
       "Outcome Type             0\n",
       "Sex upon Outcome         0\n",
       "Name                     0\n",
       "Found Location           0\n",
       "Intake Type              0\n",
       "Intake Condition         0\n",
       "Pet Type                 0\n",
       "Sex upon Intake          0\n",
       "Breed                    0\n",
       "Color                    0\n",
       "Age upon Intake Days     0\n",
       "Age upon Outcome Days    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_dropped.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59118, 13)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_dropped.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This approach can dramatically reduce the number of data samples. This can sometimes lead to overfitting models -- especially when the number of features is greater or comparable to the number of data samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"53\">Impute (fill-in) missing values with .fillna()</a>\n",
    "(<a href=\"#5\">Go to Handling Missing Values</a>)\n",
    "\n",
    "Rather than dropping rows (data samples) and/or columns (features), another strategy to deal with missing values would be to actually complete the missing values with new values: imputation of missing values.\n",
    "\n",
    "__Imputing Numerical Values:__ The easiest way to impute numerical values is to get the __average (mean) value__ for the corresponding column and use that as the new value for each missing record in that column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Intake Days     0\n",
      "Age upon Outcome Days    0\n",
      "dtype: int64\n",
      "Age upon Intake Days     0\n",
      "Age upon Outcome Days    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Impute numerical features by using the mean per feature to replace the nans\n",
    "\n",
    "# Assign our df to a new df \n",
    "df_imputed = df.copy()\n",
    "print(df_imputed[numerical_features_all].isna().sum())\n",
    "\n",
    "# Impute our two numerical features with the means. \n",
    "df_imputed[numerical_features_all] = df_imputed[numerical_features_all].fillna(df_imputed[numerical_features_all].mean())\n",
    "\n",
    "print(df_imputed[numerical_features_all].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Imputing Categorical Values:__ We can impute categorical values by getting the most common (mode) value for the corresponding column and use that as the new value for each missing record in that column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pet ID                  0\n",
      "Sex upon Outcome        1\n",
      "Name                36343\n",
      "Found Location          0\n",
      "Intake Type             0\n",
      "Intake Condition        0\n",
      "Pet Type                0\n",
      "Sex upon Intake         1\n",
      "Breed                   0\n",
      "Color                   0\n",
      "dtype: int64\n",
      "Pet ID 0        A047759\n",
      "1        A134067\n",
      "2        A141142\n",
      "3        A163459\n",
      "4        A165752\n",
      "          ...   \n",
      "95457    A819862\n",
      "95458    A819864\n",
      "95459    A819865\n",
      "95460    A819895\n",
      "95461    A819908\n",
      "Name: Pet ID, Length: 95462, dtype: object\n",
      "Sex upon Outcome 0    Neutered Male\n",
      "Name: Sex upon Outcome, dtype: object\n",
      "Name 0    Bella\n",
      "Name: Name, dtype: object\n",
      "Found Location 0    Austin (TX)\n",
      "Name: Found Location, dtype: object\n",
      "Intake Type 0    Stray\n",
      "Name: Intake Type, dtype: object\n",
      "Intake Condition 0    Normal\n",
      "Name: Intake Condition, dtype: object\n",
      "Pet Type 0    Dog\n",
      "Name: Pet Type, dtype: object\n",
      "Sex upon Intake 0    Intact Male\n",
      "Name: Sex upon Intake, dtype: object\n",
      "Breed 0    Domestic Shorthair Mix\n",
      "Name: Breed, dtype: object\n",
      "Color 0    Black/White\n",
      "Name: Color, dtype: object\n",
      "Pet ID              0\n",
      "Sex upon Outcome    0\n",
      "Name                0\n",
      "Found Location      0\n",
      "Intake Type         0\n",
      "Intake Condition    0\n",
      "Pet Type            0\n",
      "Sex upon Intake     0\n",
      "Breed               0\n",
      "Color               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Impute categorical features by using the mode per feature to replace the nans\n",
    "\n",
    "# Assign our df to a new df \n",
    "df_imputed_c = df.copy()\n",
    "print(df_imputed_c[categorical_features_all].isna().sum())\n",
    "\n",
    "for c in categorical_features_all:\n",
    "    # Find the mode per each feature\n",
    "    mode_impute = df_imputed_c[c].mode()\n",
    "    print(c, mode_impute)\n",
    "\n",
    "    # Impute our categorical features with the mode\n",
    "    # \"inplace=True\" parameter replaces missing values in place (no need for left handside assignment)\n",
    "    df_imputed_c[c].fillna(False, inplace=True)\n",
    "\n",
    "print(df_imputed_c[categorical_features_all].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create a new category, such as \"Missing\", for alll or elected categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pet ID                  0\n",
      "Sex upon Outcome        1\n",
      "Name                36343\n",
      "Found Location          0\n",
      "Intake Type             0\n",
      "Intake Condition        0\n",
      "Pet Type                0\n",
      "Sex upon Intake         1\n",
      "Breed                   0\n",
      "Color                   0\n",
      "dtype: int64\n",
      "Pet ID              0\n",
      "Sex upon Outcome    0\n",
      "Name                0\n",
      "Found Location      0\n",
      "Intake Type         0\n",
      "Intake Condition    0\n",
      "Pet Type            0\n",
      "Sex upon Intake     0\n",
      "Breed               0\n",
      "Color               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Impute categorical features by using a placeholder value\n",
    "\n",
    "# Assign our df to a new df \n",
    "df_imputed = df.copy()\n",
    "print(df_imputed[categorical_features_all].isna().sum())\n",
    "\n",
    "# Impute our categorical features with a new category named \"Missing\". \n",
    "df_imputed[categorical_features_all]= df_imputed[categorical_features_all].fillna(\"Missing\")\n",
    "\n",
    "print(df_imputed[categorical_features_all].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"54\">Impute (fill-in) missing values with sklearn's __SimpleImputer__</a>\n",
    "(<a href=\"#5\">Go to Handling Missing Values</a>)\n",
    "\n",
    "A more elegant way to implement imputation is using sklearn's __SimpleImputer__, a class implementing .fit() and .transform() methods.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age upon Intake Days     0\n",
      "Age upon Outcome Days    0\n",
      "dtype: int64\n",
      "Age upon Intake Days     0\n",
      "Age upon Outcome Days    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Impute numerical columns by using the mean per column to replace the nans\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Assign our df to a new df\n",
    "df_sklearn_imputed = df.copy()\n",
    "print(df_sklearn_imputed[numerical_features_all].isna().sum())\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "df_sklearn_imputed[numerical_features_all] = imputer.fit_transform(df_sklearn_imputed[numerical_features_all])\n",
    "\n",
    "print(df_sklearn_imputed[numerical_features_all].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Name', 'Sex upon Intake'], dtype='object')\n",
      "Name               36343\n",
      "Sex upon Intake        1\n",
      "dtype: int64\n",
      "Name               0\n",
      "Sex upon Intake    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Impute categorical columns by using the mode per column to replace the nans\n",
    "\n",
    "# Pick some categorical features you desire to impute with this approach\n",
    "categoricals_missing_values = df[categorical_features_all].loc[:,list(((df[categorical_features_all].isna().sum()/len(df.index)) > 0.0))].columns    \n",
    "columns_to_impute = categoricals_missing_values[1:3]\n",
    "print(columns_to_impute)\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Assign our df to a new df\n",
    "df_sklearn_imputer = df.copy()\n",
    "print(df_sklearn_imputer[columns_to_impute].isna().sum())\n",
    "\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "df_sklearn_imputer[columns_to_impute] = imputer.fit_transform(df_sklearn_imputer[columns_to_impute])\n",
    "\n",
    "print(df_sklearn_imputer[columns_to_impute].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Name', 'Sex upon Intake'], dtype='object')\n",
      "Name               36343\n",
      "Sex upon Intake        1\n",
      "dtype: int64\n",
      "Name               0\n",
      "Sex upon Intake    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Impute categorical columns by using a placeholder \"Missing\"\n",
    "\n",
    "# Pick some categorical features you desire to impute with this approach\n",
    "categoricals_missing_values = df[categorical_features_all].loc[:,list(((df[categorical_features_all].isna().sum()/len(df.index)) > 0.0))].columns    \n",
    "columns_to_impute = categoricals_missing_values[1:3]\n",
    "print(columns_to_impute)\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Assign our df to a new df\n",
    "df_sklearn_imputer = df.copy()\n",
    "print(df_sklearn_imputer[columns_to_impute].isna().sum())\n",
    "\n",
    "imputer = SimpleImputer(strategy='constant', fill_value = \"Missing\")\n",
    "df_sklearn_imputer[columns_to_impute] = imputer.fit_transform(df_sklearn_imputer[columns_to_impute])\n",
    "\n",
    "print(df_sklearn_imputer[columns_to_impute].isna().sum())"
   ]
  }
 ],
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